While most of the cost functions assume a nonnegative matrix of pairwise similarities as input, Correlation Clustering assumes that the similarities can be negative as well. This cost function was first introduced on graphs with only \(+1\)  and \(-1\)  edge weights [1]}, and then it was generalized to graphs with arbitrary positive and negative edge weights [2]}.
